Advanced Signal Processing Handbook, 1st Edition by Stergios Stergiopoulos

By Stergios Stergiopoulos

Advances in electronic sign processing algorithms and computing device know-how have mixed to supply real-time platforms with services a long way past these of simply few years in the past. Nonlinear, adaptive tools for sign processing have emerged to supply higher array achieve functionality, even if, they lack the robustness of traditional algorithms. The problem is still to increase an idea that exploits some great benefits of both-a scheme that integrates those equipment in useful, real-time systems.The complicated sign Processing guide is helping you meet that problem. past providing a great creation to the foundations and purposes of complicated sign processing, it develops a common processing constitution that takes benefit of the similarities that exist between radar, sonar, and scientific imaging platforms and integrates traditional and nonlinear processing schemes.

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Stansfield, Underwater Electroacoustic Transducers, Bath University Press and Institute of Acoustics, 1990. 17. M. T. M. M. , Chapman & Hall, New York, 1988. 18. B. A. E. P. M. Mueller, The NMR phased array, Magn. Reson. , 16, 192–225, 1990. 19. S. A. V. Mulkern, Partial RF echo planar imaging with the FAISE method. I. Experimental and theoretical assessment of artifact, Magn. Reson. , 26, 328–341, 1992. 20. L. Owsley, Sonar Array Processing, S. V. , p. 123, Prentice-Hall, Englewood Cliffs, NJ, 1985.

Multiplications, divisions, and additions/subtractions) required to make one complete iteration of the algorithm, (2) the size of memory locations required to store the data and the program, and (3) the investment required to program the algorithm on a computer. • Structure — This refers to the structure of information flow in the algorithm, determining the manner in which it is implemented in hardware form. * • Numerical properties — When an algorithm is implemented numerically, inaccuracies are produced due to quantization errors.

For a detailed treatment of the subject, see Haykin (1999); this book has an up-to-date bibliography that occupies 41 pages of references. In the context of adaptive signal-processing applications, neural networks offer the following advantages: • Nonlinearity, which makes it possible to account for the nonlinear behavior of physical phenomena responsible for generating the input data • The ability to approximate any prescribed input-output mapping of a continuous nature • Weak statistical assumptions about the environment, in which the network is embedded • Learning capability, which is accomplished by undertaking a training session with input-output examples that are representative of the environment • Generalization, which refers to the ability of the neural network to provide a satisfactory performance in response to test data never seen by the network before • Fault tolerance, which means that the network continues to provide an acceptable performance despite the failure of some neurons in the network • VLSI implementability, which exploits the massive parallelism built into the design of a neural network ©2001 CRC Press LLC This is indeed an impressive list of attributes, which accounts for the widespread interest in the use of neural networks to solve signal-processing tasks that are too difficult for conventional (linear) adaptive filters.

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